This disclosure generally relates to content identification, and more specifically to detecting commercials in media streams based on audio fingerprinting.
Commercial detection in media streams has become increasingly important because many media streams, such as TV broadcasting, include commercials between segments of media programs. A media stream can be an audio stream, a video stream or a combined audio and video stream (also called “audio-visual stream”). Commercials can appear in audio streams or audio-visual streams, such as advertisements on broadcast radio and television stations, and songs or video on music channels.
Existing content-based identification systems use various approaches, such as a feature-based approach and a recognition based approach to detect commercials in media streams. A feature-based approach typically uses some inherent characteristics of TV commercials to differentiate commercials from non-commercial media content. For example, a feature-based approach may rely on the detection of scene changes in video frames or the detection of black frames at the beginning and end of a TV commercial. A recognition based approach attempts to identify commercials in a media stream using a database of known commercials. However, both approaches are computationally expensive and often require large storage space.
Another method of content-based identification is audio fingerprinting. An audio fingerprint is a compact summary of an audio signal that can be used to perform content-based identification. For example, an audio fingerprint of an unidentified audio signal is compared to reference audio fingerprints for identification of the audio signal. Some existing solutions of commercial detection using audio fingerprints of an audio portion of a media stream often generate an unnecessarily large number of false positive identifications because the existing solutions fail to differentiate commercial content from repeating real media content. For example, a signature tune of a particular TV program may repeat each time the particular TV program is aired. Thus, existing solutions of commercial detection using audio fingerprints fail to accurately detect commercials in media streams.